Overview

Dataset statistics

Number of variables33
Number of observations96022
Missing cells512704
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.5 MiB
Average record size in memory475.0 B

Variable types

Numeric18
Categorical15

Alerts

Annual_Income is highly correlated with Monthly_Inhand_Salary and 2 other fieldsHigh correlation
Monthly_Inhand_Salary is highly correlated with Annual_Income and 2 other fieldsHigh correlation
Num_Bank_Accounts is highly correlated with Num_Credit_Card and 8 other fieldsHigh correlation
Interest_Rate is highly correlated with Num_Bank_Accounts and 9 other fieldsHigh correlation
Num_of_Loan is highly correlated with Num_Bank_Accounts and 13 other fieldsHigh correlation
Delay_from_due_date is highly correlated with Num_Bank_Accounts and 7 other fieldsHigh correlation
Num_of_Delayed_Payment is highly correlated with Num_Bank_Accounts and 5 other fieldsHigh correlation
Num_Credit_Inquiries is highly correlated with Num_Bank_Accounts and 7 other fieldsHigh correlation
Outstanding_Debt is highly correlated with Num_Bank_Accounts and 10 other fieldsHigh correlation
Total_EMI_per_month is highly correlated with Num_of_Loan and 1 other fieldsHigh correlation
Amount_invested_monthly is highly correlated with Annual_Income and 1 other fieldsHigh correlation
Monthly_Balance is highly correlated with Annual_Income and 1 other fieldsHigh correlation
Credit_History_Age_Numeric is highly correlated with Interest_Rate and 5 other fieldsHigh correlation
No Loan is highly correlated with Num_of_Loan and 1 other fieldsHigh correlation
Credit_Mix is highly correlated with Num_Bank_Accounts and 10 other fieldsHigh correlation
Num_Credit_Card is highly correlated with Num_Bank_Accounts and 4 other fieldsHigh correlation
Changed_Credit_Limit is highly correlated with Outstanding_Debt and 1 other fieldsHigh correlation
Credit-Builder Loan is highly correlated with Num_of_LoanHigh correlation
Home Equity Loan is highly correlated with Num_of_LoanHigh correlation
Mortgage Loan is highly correlated with Num_of_LoanHigh correlation
Payday Loan is highly correlated with Num_of_LoanHigh correlation
Payment_of_Min_Amount is highly correlated with Num_Bank_Accounts and 7 other fieldsHigh correlation
Age has 16022 (16.7%) missing values Missing
Annual_Income has 16022 (16.7%) missing values Missing
Monthly_Inhand_Salary has 16022 (16.7%) missing values Missing
Num_Bank_Accounts has 16022 (16.7%) missing values Missing
Num_Credit_Card has 16022 (16.7%) missing values Missing
Interest_Rate has 16022 (16.7%) missing values Missing
Num_of_Loan has 16022 (16.7%) missing values Missing
Delay_from_due_date has 16022 (16.7%) missing values Missing
Num_of_Delayed_Payment has 16022 (16.7%) missing values Missing
Changed_Credit_Limit has 16022 (16.7%) missing values Missing
Num_Credit_Inquiries has 16022 (16.7%) missing values Missing
Outstanding_Debt has 16022 (16.7%) missing values Missing
Credit_Utilization_Ratio has 16022 (16.7%) missing values Missing
Total_EMI_per_month has 16022 (16.7%) missing values Missing
Amount_invested_monthly has 16022 (16.7%) missing values Missing
Monthly_Balance has 16022 (16.7%) missing values Missing
Auto Loan has 16022 (16.7%) missing values Missing
Credit-Builder Loan has 16022 (16.7%) missing values Missing
Personal Loan has 16022 (16.7%) missing values Missing
Home Equity Loan has 16022 (16.7%) missing values Missing
Not Specified has 16022 (16.7%) missing values Missing
Mortgage Loan has 16022 (16.7%) missing values Missing
Student Loan has 16022 (16.7%) missing values Missing
Debt Consolidation Loan has 16022 (16.7%) missing values Missing
Payday Loan has 16022 (16.7%) missing values Missing
No Loan has 16022 (16.7%) missing values Missing
Credit_History_Age_Numeric has 16022 (16.7%) missing values Missing
Occupation has 16022 (16.7%) missing values Missing
Credit_Mix has 16022 (16.7%) missing values Missing
Payment_of_Min_Amount has 16022 (16.7%) missing values Missing
Payment_Behaviour has 16022 (16.7%) missing values Missing
Credit_Score has 16022 (16.7%) missing values Missing
df_index has unique values Unique
Num_Bank_Accounts has 3460 (3.6%) zeros Zeros
Num_of_Loan has 8339 (8.7%) zeros Zeros
Delay_from_due_date has 979 (1.0%) zeros Zeros
Num_of_Delayed_Payment has 1245 (1.3%) zeros Zeros
Num_Credit_Inquiries has 5637 (5.9%) zeros Zeros
Total_EMI_per_month has 8506 (8.9%) zeros Zeros

Reproduction

Analysis started2022-10-25 19:40:56.679303
Analysis finished2022-10-25 19:42:27.675871
Duration1 minute and 31 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct96022
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48341.61973
Minimum0
Maximum99998
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:27.806309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4801.05
Q124005.25
median48010.5
Q372015.75
95-th percentile94007.95
Maximum99998
Range99998
Interquartile range (IQR)48010.5

Descriptive statistics

Standard deviation28238.61453
Coefficient of variation (CV)0.5841470494
Kurtosis-1.149008265
Mean48341.61973
Median Absolute Deviation (MAD)24005.5
Skewness0.05122370091
Sum4641859010
Variance797419350.5
MonotonicityStrictly increasing
2022-10-25T21:42:27.994975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
640121
 
< 0.1%
640211
 
< 0.1%
640201
 
< 0.1%
640191
 
< 0.1%
640181
 
< 0.1%
640171
 
< 0.1%
640161
 
< 0.1%
640151
 
< 0.1%
640141
 
< 0.1%
Other values (96012)96012
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
999981
< 0.1%
999971
< 0.1%
999961
< 0.1%
999951
< 0.1%
999941
< 0.1%
999921
< 0.1%
999901
< 0.1%
999891
< 0.1%
999881
< 0.1%
999871
< 0.1%

Age
Real number (ℝ≥0)

MISSING

Distinct42
Distinct (%)0.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean33.528025
Minimum15
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:28.162210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q126
median33
Q341
95-th percentile52
Maximum56
Range41
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.13701628
Coefficient of variation (CV)0.3023445694
Kurtosis-0.7308763086
Mean33.528025
Median Absolute Deviation (MAD)8
Skewness0.1928563768
Sum2682242
Variance102.7590991
MonotonicityNot monotonic
2022-10-25T21:42:28.307479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
339024
 
9.4%
282280
 
2.4%
312244
 
2.3%
382242
 
2.3%
322217
 
2.3%
262214
 
2.3%
252198
 
2.3%
272186
 
2.3%
362174
 
2.3%
342171
 
2.3%
Other values (32)51050
53.2%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
151185
1.2%
161085
1.1%
171151
1.2%
181810
1.9%
192094
2.2%
202107
2.2%
212023
2.1%
222082
2.2%
232025
2.1%
242048
2.1%
ValueCountFrequency (%)
56267
 
0.3%
551048
1.1%
54992
1.0%
531045
1.1%
521026
1.1%
51970
1.0%
50963
1.0%
491059
1.1%
481071
1.1%
47931
1.0%

Annual_Income
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12267
Distinct (%)15.3%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean47328.22288
Minimum7005.93
Maximum153147.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:28.453301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7005.93
5-th percentile9917.025
Q120082.6325
median36496.38
Q365934.22
95-th percentile121187.49
Maximum153147.64
Range146141.71
Interquartile range (IQR)45851.5875

Descriptive statistics

Standard deviation33680.6717
Coefficient of variation (CV)0.7116403205
Kurtosis0.4476756089
Mean47328.22288
Median Absolute Deviation (MAD)19096.95
Skewness1.108716132
Sum3786257830
Variance1134387646
MonotonicityNot monotonic
2022-10-25T21:42:28.621035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36496.387598
 
7.9%
20867.6715
 
< 0.1%
17273.8314
 
< 0.1%
95596.3514
 
< 0.1%
33029.6613
 
< 0.1%
72524.213
 
< 0.1%
9141.6313
 
< 0.1%
36585.1213
 
< 0.1%
17816.7512
 
< 0.1%
22434.1612
 
< 0.1%
Other values (12257)72283
75.3%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
7005.934
< 0.1%
7006.0357
< 0.1%
7006.527
< 0.1%
7011.6857
< 0.1%
7012.317
< 0.1%
7019.4356
< 0.1%
7020.5456
< 0.1%
7021.918
< 0.1%
7023.167
< 0.1%
7039.7456
< 0.1%
ValueCountFrequency (%)
153147.645
< 0.1%
152947.125
< 0.1%
152895.447
< 0.1%
152796.762
 
< 0.1%
152574.764
< 0.1%
152483.326
< 0.1%
152340.567
< 0.1%
152252.245
< 0.1%
152148.928
< 0.1%
152104.685
< 0.1%

Monthly_Inhand_Salary
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13165
Distinct (%)16.5%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean4038.992109
Minimum303.6454167
Maximum15204.63333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:28.783609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum303.6454167
5-th percentile893.8820833
Q11801.546667
median3106.8475
Q35387.345
95-th percentile10474.35667
Maximum15204.63333
Range14900.98792
Interquartile range (IQR)3585.798333

Descriptive statistics

Standard deviation2959.486161
Coefficient of variation (CV)0.7327288791
Kurtosis1.366820583
Mean4038.992109
Median Absolute Deviation (MAD)1552.665
Skewness1.342870444
Sum323119368.7
Variance8758558.335
MonotonicityNot monotonic
2022-10-25T21:42:28.949139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3106.847511991
 
12.5%
6769.1314
 
< 0.1%
2295.05833314
 
< 0.1%
6082.187514
 
< 0.1%
3080.55512
 
< 0.1%
6358.95666711
 
< 0.1%
6639.5611
 
< 0.1%
5766.49166710
 
< 0.1%
1315.56083310
 
< 0.1%
4387.27259
 
< 0.1%
Other values (13155)67904
70.7%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
303.64541676
< 0.1%
319.556255
< 0.1%
332.12833334
< 0.1%
332.431256
< 0.1%
333.59666675
< 0.1%
355.20833336
< 0.1%
357.25583337
< 0.1%
358.05833334
< 0.1%
361.60333334
< 0.1%
368.37416676
< 0.1%
ValueCountFrequency (%)
15204.633337
< 0.1%
15167.187
< 0.1%
15136.696675
< 0.1%
15115.195
< 0.1%
15101.945
< 0.1%
15091.086674
< 0.1%
15090.076676
< 0.1%
15066.783334
< 0.1%
15038.316672
 
< 0.1%
14978.336675
< 0.1%

Num_Bank_Accounts
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean5.3593125
Minimum0
Maximum11
Zeros3460
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:29.116595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.575687278
Coefficient of variation (CV)0.4806003154
Kurtosis-0.665167121
Mean5.3593125
Median Absolute Deviation (MAD)2
Skewness-0.1818061607
Sum428745
Variance6.634164954
MonotonicityNot monotonic
2022-10-25T21:42:29.239727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
510819
11.3%
610415
10.8%
710207
10.6%
810194
10.6%
49731
10.1%
39560
10.0%
94317
 
4.5%
104208
 
4.4%
13619
 
3.8%
23463
 
3.6%
Other values (2)3467
 
3.6%
(Missing)16022
16.7%
ValueCountFrequency (%)
03460
 
3.6%
13619
 
3.8%
23463
 
3.6%
39560
10.0%
49731
10.1%
510819
11.3%
610415
10.8%
710207
10.6%
810194
10.6%
94317
 
4.5%
ValueCountFrequency (%)
117
 
< 0.1%
104208
 
4.4%
94317
 
4.5%
810194
10.6%
710207
10.6%
610415
10.8%
510819
11.3%
49731
10.1%
39560
10.0%
23463
 
3.6%

Num_Credit_Card
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean5.5188125
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:29.350313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q37
95-th percentile9
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.044803989
Coefficient of variation (CV)0.3705152131
Kurtosis-0.2703626287
Mean5.5188125
Median Absolute Deviation (MAD)1
Skewness0.247154058
Sum441505
Variance4.181223355
MonotonicityNot monotonic
2022-10-25T21:42:29.480242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
516547
17.2%
713312
13.9%
613259
13.8%
411254
11.7%
310620
11.1%
83947
 
4.1%
103877
 
4.0%
93701
 
3.9%
21738
 
1.8%
11717
 
1.8%
(Missing)16022
16.7%
ValueCountFrequency (%)
11717
 
1.8%
21738
 
1.8%
310620
11.1%
411254
11.7%
516547
17.2%
613259
13.8%
713312
13.9%
83947
 
4.1%
93701
 
3.9%
103877
 
4.0%
ValueCountFrequency (%)
1128
 
< 0.1%
103877
 
4.0%
93701
 
3.9%
83947
 
4.1%
713312
13.9%
613259
13.8%
516547
17.2%
411254
11.7%
310620
11.1%
21738
 
1.8%

Interest_Rate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean14.4719375
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:29.630211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median13
Q320
95-th percentile31
Maximum34
Range33
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.658508799
Coefficient of variation (CV)0.5982964478
Kurtosis-0.6213051615
Mean14.4719375
Median Absolute Deviation (MAD)6
Skewness0.5176800417
Sum1157755
Variance74.96977462
MonotonicityNot monotonic
2022-10-25T21:42:29.785741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
83992
 
4.2%
53974
 
4.1%
63816
 
4.0%
103639
 
3.8%
93630
 
3.8%
123628
 
3.8%
73595
 
3.7%
133538
 
3.7%
113538
 
3.7%
183266
 
3.4%
Other values (24)43384
45.2%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
12158
2.2%
22026
2.1%
32218
2.3%
42079
2.2%
53974
4.1%
63816
4.0%
73595
3.7%
83992
4.2%
93630
3.8%
103639
3.8%
ValueCountFrequency (%)
341210
1.3%
331167
1.2%
321389
1.4%
311188
1.2%
301356
1.4%
291332
1.4%
281277
1.3%
271262
1.3%
261187
1.2%
251228
1.3%

Num_of_Loan
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean3.4785875
Minimum0
Maximum9
Zeros8339
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:29.927323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.338040811
Coefficient of variation (CV)0.6721236166
Kurtosis-0.3513117617
Mean3.4785875
Median Absolute Deviation (MAD)1
Skewness0.5293797177
Sum278287
Variance5.466434835
MonotonicityNot monotonic
2022-10-25T21:42:30.034363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
318800
19.6%
211426
11.9%
411165
11.6%
08339
8.7%
18121
8.5%
65925
 
6.2%
75539
 
5.8%
55456
 
5.7%
92819
 
2.9%
82410
 
2.5%
(Missing)16022
16.7%
ValueCountFrequency (%)
08339
8.7%
18121
8.5%
211426
11.9%
318800
19.6%
411165
11.6%
55456
 
5.7%
65925
 
6.2%
75539
 
5.8%
82410
 
2.5%
92819
 
2.9%
ValueCountFrequency (%)
92819
 
2.9%
82410
 
2.5%
75539
 
5.8%
65925
 
6.2%
55456
 
5.7%
411165
11.6%
318800
19.6%
211426
11.9%
18121
8.5%
08339
8.7%

Delay_from_due_date
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct68
Distinct (%)0.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean21.157175
Minimum0
Maximum67
Zeros979
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:30.173404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median18
Q328
95-th percentile54
Maximum67
Range67
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.73602763
Coefficient of variation (CV)0.6965026112
Kurtosis0.3884490529
Mean21.157175
Median Absolute Deviation (MAD)9
Skewness0.9895904879
Sum1692574
Variance217.1505104
MonotonicityNot monotonic
2022-10-25T21:42:30.340200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152869
 
3.0%
132731
 
2.8%
82646
 
2.8%
102637
 
2.7%
142625
 
2.7%
92609
 
2.7%
182598
 
2.7%
72581
 
2.7%
112570
 
2.7%
62524
 
2.6%
Other values (58)53610
55.8%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
0979
 
1.0%
11056
 
1.1%
21057
 
1.1%
31369
1.4%
41382
1.4%
52428
2.5%
62524
2.6%
72581
2.7%
82646
2.8%
92609
2.7%
ValueCountFrequency (%)
6719
 
< 0.1%
6625
 
< 0.1%
6546
 
< 0.1%
6454
 
0.1%
6354
 
0.1%
62424
0.4%
61418
0.4%
60413
0.4%
59400
0.4%
58444
0.5%

Num_of_Delayed_Payment
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct29
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean13.4739125
Minimum0
Maximum28
Zeros1245
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:30.486380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median14
Q318
95-th percentile23
Maximum28
Range28
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.852582558
Coefficient of variation (CV)0.4343640022
Kurtosis-0.3687713316
Mean13.4739125
Median Absolute Deviation (MAD)4
Skewness-0.2192224617
Sum1077913
Variance34.2527226
MonotonicityNot monotonic
2022-10-25T21:42:30.616352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1412140
 
12.6%
194250
 
4.4%
174224
 
4.4%
164196
 
4.4%
104107
 
4.3%
154032
 
4.2%
184012
 
4.2%
123932
 
4.1%
203926
 
4.1%
93908
 
4.1%
Other values (19)31273
32.6%
(Missing)16022
16.7%
ValueCountFrequency (%)
01245
 
1.3%
11277
 
1.3%
21395
 
1.5%
31483
 
1.5%
41459
 
1.5%
51658
1.7%
61795
1.9%
71846
1.9%
83781
3.9%
93908
4.1%
ValueCountFrequency (%)
28105
 
0.1%
27185
 
0.2%
26249
 
0.3%
251283
 
1.3%
241299
 
1.4%
231575
 
1.6%
221782
1.9%
212034
2.1%
203926
4.1%
194250
4.4%

Changed_Credit_Limit
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3627
Distinct (%)4.5%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean10.54526363
Minimum0
Maximum36.97
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:30.782282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q15.76
median9.51
Q314.61
95-th percentile23.44
Maximum36.97
Range36.97
Interquartile range (IQR)8.85

Descriptive statistics

Standard deviation6.510016205
Coefficient of variation (CV)0.6173402995
Kurtosis0.2395578803
Mean10.54526363
Median Absolute Deviation (MAD)4.25
Skewness0.7541588219
Sum843621.09
Variance42.38031099
MonotonicityNot monotonic
2022-10-25T21:42:30.945563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.513035
 
3.2%
8.22104
 
0.1%
11.5104
 
0.1%
11.32103
 
0.1%
10.06100
 
0.1%
7.3597
 
0.1%
8.2394
 
0.1%
1.6391
 
0.1%
7.6991
 
0.1%
7.3390
 
0.1%
Other values (3617)76091
79.2%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
04
< 0.1%
0.017
< 0.1%
0.021
 
< 0.1%
0.023
< 0.1%
0.031
 
< 0.1%
0.031
 
< 0.1%
0.033
< 0.1%
0.042
 
< 0.1%
0.053
< 0.1%
0.063
< 0.1%
ValueCountFrequency (%)
36.971
< 0.1%
36.491
< 0.1%
36.091
< 0.1%
35.841
< 0.1%
35.821
< 0.1%
35.521
< 0.1%
35.41
< 0.1%
35.31
< 0.1%
35.281
< 0.1%
35.021
< 0.1%

Num_Credit_Inquiries
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct18
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean5.7419
Minimum0
Maximum17
Zeros5637
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:31.080932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q38
95-th percentile12
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.789375131
Coefficient of variation (CV)0.6599514326
Kurtosis-0.5260074897
Mean5.7419
Median Absolute Deviation (MAD)3
Skewness0.4342370186
Sum459352
Variance14.35936388
MonotonicityNot monotonic
2022-10-25T21:42:31.208570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
49052
9.4%
57484
7.8%
37131
7.4%
66516
6.8%
76420
6.7%
26394
 
6.7%
86250
 
6.5%
16059
 
6.3%
05637
 
5.9%
94225
 
4.4%
Other values (8)14832
15.4%
(Missing)16022
16.7%
ValueCountFrequency (%)
05637
5.9%
16059
6.3%
26394
6.7%
37131
7.4%
49052
9.4%
57484
7.8%
66516
6.8%
76420
6.7%
86250
6.5%
94225
4.4%
ValueCountFrequency (%)
17220
 
0.2%
16351
 
0.4%
15624
 
0.6%
14834
 
0.9%
131198
 
1.2%
123676
3.8%
114055
4.2%
103874
4.0%
94225
4.4%
86250
6.5%

Outstanding_Debt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12203
Distinct (%)15.3%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean1420.501156
Minimum0.23
Maximum4998.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:31.371893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile121.26
Q1570.4
median1161.51
Q31920.6875
95-th percentile4071.62
Maximum4998.07
Range4997.84
Interquartile range (IQR)1350.2875

Descriptive statistics

Standard deviation1148.662237
Coefficient of variation (CV)0.8086316806
Kurtosis0.9816417127
Mean1420.501156
Median Absolute Deviation (MAD)631.64
Skewness1.228487765
Sum113640092.5
Variance1319424.936
MonotonicityNot monotonic
2022-10-25T21:42:31.538277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1161.51828
 
0.9%
1360.4520
 
< 0.1%
1151.718
 
< 0.1%
460.4618
 
< 0.1%
1263.1816
 
< 0.1%
1109.0316
 
< 0.1%
1406.8516
 
< 0.1%
1961.7316
 
< 0.1%
1286.0716
 
< 0.1%
796.8815
 
< 0.1%
Other values (12193)79021
82.3%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
0.238
< 0.1%
0.347
< 0.1%
0.547
< 0.1%
0.566
< 0.1%
0.777
< 0.1%
0.9514
< 0.1%
1.22
 
< 0.1%
1.238
< 0.1%
1.36
< 0.1%
1.335
 
< 0.1%
ValueCountFrequency (%)
4998.077
< 0.1%
4997.17
< 0.1%
4997.058
< 0.1%
4992.258
< 0.1%
4990.918
< 0.1%
4987.195
< 0.1%
4986.036
< 0.1%
4984.826
< 0.1%
4983.867
< 0.1%
4982.577
< 0.1%

Credit_Utilization_Ratio
Real number (ℝ≥0)

MISSING

Distinct80000
Distinct (%)100.0%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean32.28648288
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:31.710350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24.24040883
Q128.053517
median32.30396013
Q336.50004369
95-th percentile40.23264821
Maximum50
Range30
Interquartile range (IQR)8.446526691

Descriptive statistics

Standard deviation5.114983442
Coefficient of variation (CV)0.1584249192
Kurtosis-0.9431484111
Mean32.28648288
Median Absolute Deviation (MAD)4.222493097
Skewness0.02906363257
Sum2582918.631
Variance26.16305561
MonotonicityNot monotonic
2022-10-25T21:42:31.868505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.078445951
 
< 0.1%
29.163550441
 
< 0.1%
31.499903861
 
< 0.1%
26.115215171
 
< 0.1%
25.02230111
 
< 0.1%
33.130147021
 
< 0.1%
31.625560841
 
< 0.1%
35.76572411
 
< 0.1%
39.719989991
 
< 0.1%
27.769679251
 
< 0.1%
Other values (79990)79990
83.3%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
201
< 0.1%
20.244130351
< 0.1%
20.83094561
< 0.1%
20.832487091
< 0.1%
20.880081931
< 0.1%
20.881250041
< 0.1%
20.985605791
< 0.1%
20.9929141
< 0.1%
21.027664511
< 0.1%
21.028690261
< 0.1%
ValueCountFrequency (%)
501
< 0.1%
49.564519351
< 0.1%
49.52232431
< 0.1%
49.254982981
< 0.1%
49.064277451
< 0.1%
48.489851731
< 0.1%
48.247002521
< 0.1%
48.199823981
< 0.1%
48.19174891
< 0.1%
48.17659891
< 0.1%

Total_EMI_per_month
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct11152
Distinct (%)13.9%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean88.04881593
Minimum0
Maximum358.8601966
Zeros8506
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:32.046769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130.30645981
median63.36866264
Q3125.0155077
95-th percentile260.468081
Maximum358.8601966
Range358.8601966
Interquartile range (IQR)94.70904786

Descriptive statistics

Standard deviation80.06974018
Coefficient of variation (CV)0.9093789546
Kurtosis0.8679143271
Mean88.04881593
Median Absolute Deviation (MAD)40.09474823
Skewness1.226496118
Sum7043905.275
Variance6411.163293
MonotonicityNot monotonic
2022-10-25T21:42:32.205302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08506
 
8.9%
63.368662645474
 
5.7%
21.397139478
 
< 0.1%
52.923806568
 
< 0.1%
43.495717738
 
< 0.1%
44.311758568
 
< 0.1%
48.24567148
 
< 0.1%
76.820313548
 
< 0.1%
46.471196988
 
< 0.1%
153.31624618
 
< 0.1%
Other values (11142)65956
68.7%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
08506
8.9%
4.4628374675
 
< 0.1%
4.7131835726
 
< 0.1%
4.8656896777
 
< 0.1%
4.9161385423
 
< 0.1%
5.1384846967
 
< 0.1%
5.2184663597
 
< 0.1%
5.249273276
 
< 0.1%
5.2622910487
 
< 0.1%
5.3510861517
 
< 0.1%
ValueCountFrequency (%)
358.86019666
< 0.1%
358.04056525
< 0.1%
357.40606754
< 0.1%
356.9795435
< 0.1%
356.93164696
< 0.1%
356.76653256
< 0.1%
356.6974594
< 0.1%
356.36547857
< 0.1%
356.33154086
< 0.1%
356.14292925
< 0.1%

Amount_invested_monthly
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct72848
Distinct (%)91.1%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean189.9346176
Minimum0
Maximum1977.326102
Zeros146
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:32.374661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.46250811
Q177.28796563
median129.1812728
Q3220.7058888
95-th percentile588.3354755
Maximum1977.326102
Range1977.326102
Interquartile range (IQR)143.4179232

Descriptive statistics

Standard deviation191.7948737
Coefficient of variation (CV)1.009794192
Kurtosis10.03501351
Mean189.9346176
Median Absolute Deviation (MAD)62.26324442
Skewness2.736210206
Sum15194769.41
Variance36785.27358
MonotonicityNot monotonic
2022-10-25T21:42:32.556389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.18127287008
 
7.3%
0146
 
0.2%
86.991776711
 
< 0.1%
96.230965611
 
< 0.1%
10.80513591
 
< 0.1%
61.808693641
 
< 0.1%
107.19801991
 
< 0.1%
195.4756681
 
< 0.1%
40.683632491
 
< 0.1%
58.772462811
 
< 0.1%
Other values (72838)72838
75.9%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
0146
0.2%
10.010194261
 
< 0.1%
10.01142481
 
< 0.1%
10.036599611
 
< 0.1%
10.053768351
 
< 0.1%
10.071936771
 
< 0.1%
10.10754691
 
< 0.1%
10.116614041
 
< 0.1%
10.12255661
 
< 0.1%
10.131910941
 
< 0.1%
ValueCountFrequency (%)
1977.3261021
< 0.1%
1961.218851
< 0.1%
1944.5207471
< 0.1%
1941.2374541
< 0.1%
1903.0800481
< 0.1%
1901.7916951
< 0.1%
1890.8557731
< 0.1%
1885.6453181
< 0.1%
1804.3556941
< 0.1%
1804.3325271
< 0.1%

Monthly_Balance
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct79051
Distinct (%)98.8%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean401.8615211
Minimum0.007759664775
Maximum1602.040519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:32.744371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.007759664775
5-th percentile175.1027546
Q1270.8825003
median336.8486202
Q3467.947347
95-th percentile860.8077584
Maximum1602.040519
Range1602.032759
Interquartile range (IQR)197.0648467

Descriptive statistics

Standard deviation212.9496277
Coefficient of variation (CV)0.5299079821
Kurtosis3.044992976
Mean401.8615211
Median Absolute Deviation (MAD)83.58489079
Skewness1.613914025
Sum32148921.69
Variance45347.54395
MonotonicityNot monotonic
2022-10-25T21:42:32.950845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
336.8486202950
 
1.0%
291.95151741
 
< 0.1%
506.41923331
 
< 0.1%
329.08345971
 
< 0.1%
370.18411561
 
< 0.1%
436.68651281
 
< 0.1%
255.64314251
 
< 0.1%
350.71911341
 
< 0.1%
426.11875531
 
< 0.1%
659.78606741
 
< 0.1%
Other values (79041)79041
82.3%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
0.0077596647751
< 0.1%
0.088627865351
< 0.1%
0.095482496021
< 0.1%
0.13113565111
< 0.1%
0.36614707951
< 0.1%
0.38255797881
< 0.1%
0.5996401261
< 0.1%
0.63606132841
< 0.1%
0.6882987791
< 0.1%
0.71023971021
< 0.1%
ValueCountFrequency (%)
1602.0405191
< 0.1%
1567.2083091
< 0.1%
1566.6131651
< 0.1%
1564.1348261
< 0.1%
1558.4218411
< 0.1%
1555.2010511
< 0.1%
1552.9460941
< 0.1%
1546.319641
< 0.1%
1542.2746951
< 0.1%
1534.9635331
< 0.1%

Auto Loan
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
55570 
1.0
24430 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.055570
57.9%
1.024430
25.4%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:33.133830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:33.312729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.055570
69.5%
1.024430
30.5%

Most occurring characters

ValueCountFrequency (%)
0135570
56.5%
.80000
33.3%
124430
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0135570
84.7%
124430
 
15.3%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0135570
56.5%
.80000
33.3%
124430
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0135570
56.5%
.80000
33.3%
124430
 
10.2%

Credit-Builder Loan
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
54630 
1.0
25370 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.054630
56.9%
1.025370
26.4%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:33.455128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:33.630237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.054630
68.3%
1.025370
31.7%

Most occurring characters

ValueCountFrequency (%)
0134630
56.1%
.80000
33.3%
125370
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0134630
84.1%
125370
 
15.9%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0134630
56.1%
.80000
33.3%
125370
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0134630
56.1%
.80000
33.3%
125370
 
10.6%

Personal Loan
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
55139 
1.0
24861 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055139
57.4%
1.024861
25.9%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:33.819959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:33.993264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.055139
68.9%
1.024861
31.1%

Most occurring characters

ValueCountFrequency (%)
0135139
56.3%
.80000
33.3%
124861
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0135139
84.5%
124861
 
15.5%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0135139
56.3%
.80000
33.3%
124861
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0135139
56.3%
.80000
33.3%
124861
 
10.4%

Home Equity Loan
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
54820 
1.0
25180 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.054820
57.1%
1.025180
26.2%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:34.204170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:34.372662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.054820
68.5%
1.025180
31.5%

Most occurring characters

ValueCountFrequency (%)
0134820
56.2%
.80000
33.3%
125180
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0134820
84.3%
125180
 
15.7%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0134820
56.2%
.80000
33.3%
125180
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0134820
56.2%
.80000
33.3%
125180
 
10.5%

Not Specified
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
54691 
1.0
25309 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.054691
57.0%
1.025309
26.4%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:34.517379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:34.702007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.054691
68.4%
1.025309
31.6%

Most occurring characters

ValueCountFrequency (%)
0134691
56.1%
.80000
33.3%
125309
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0134691
84.2%
125309
 
15.8%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0134691
56.1%
.80000
33.3%
125309
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0134691
56.1%
.80000
33.3%
125309
 
10.5%

Mortgage Loan
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
55066 
1.0
24934 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055066
57.3%
1.024934
26.0%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:34.858281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:35.000345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.055066
68.8%
1.024934
31.2%

Most occurring characters

ValueCountFrequency (%)
0135066
56.3%
.80000
33.3%
124934
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0135066
84.4%
124934
 
15.6%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0135066
56.3%
.80000
33.3%
124934
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0135066
56.3%
.80000
33.3%
124934
 
10.4%

Student Loan
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
55175 
1.0
24825 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055175
57.5%
1.024825
25.9%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:35.121738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:35.245208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.055175
69.0%
1.024825
31.0%

Most occurring characters

ValueCountFrequency (%)
0135175
56.3%
.80000
33.3%
124825
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0135175
84.5%
124825
 
15.5%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0135175
56.3%
.80000
33.3%
124825
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0135175
56.3%
.80000
33.3%
124825
 
10.3%

Debt Consolidation Loan
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
55234 
1.0
24766 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.055234
57.5%
1.024766
25.8%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:35.366365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:35.490602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.055234
69.0%
1.024766
31.0%

Most occurring characters

ValueCountFrequency (%)
0135234
56.3%
.80000
33.3%
124766
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0135234
84.5%
124766
 
15.5%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0135234
56.3%
.80000
33.3%
124766
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0135234
56.3%
.80000
33.3%
124766
 
10.3%

Payday Loan
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
54533 
1.0
25467 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.054533
56.8%
1.025467
26.5%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:35.622148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:35.841585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.054533
68.2%
1.025467
31.8%

Most occurring characters

ValueCountFrequency (%)
0134533
56.1%
.80000
33.3%
125467
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0134533
84.1%
125467
 
15.9%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0134533
56.1%
.80000
33.3%
125467
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0134533
56.1%
.80000
33.3%
125467
 
10.6%

No Loan
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.2 MiB
0.0
70855 
1.0
9145 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.070855
73.8%
1.09145
 
9.5%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:35.980945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:36.140701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.070855
88.6%
1.09145
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0150855
62.9%
.80000
33.3%
19145
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160000
66.7%
Other Punctuation80000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0150855
94.3%
19145
 
5.7%
Other Punctuation
ValueCountFrequency (%)
.80000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0150855
62.9%
.80000
33.3%
19145
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0150855
62.9%
.80000
33.3%
19145
 
3.8%

Credit_History_Age_Numeric
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct404
Distinct (%)0.5%
Missing16022
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean18.42607708
Minimum0.08333333333
Maximum33.66666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size750.3 KiB
2022-10-25T21:42:36.303896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.08333333333
5-th percentile5.583333333
Q112.83333333
median18.25
Q324.33333333
95-th percentile31.58333333
Maximum33.66666667
Range33.58333333
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.920898049
Coefficient of variation (CV)0.4298743576
Kurtosis-0.6953209695
Mean18.42607708
Median Absolute Deviation (MAD)5.75
Skewness-0.04224457142
Sum1474086.167
Variance62.74062591
MonotonicityNot monotonic
2022-10-25T21:42:36.465356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.257582
 
7.9%
19.25361
 
0.4%
19.33333333360
 
0.4%
15.91666667358
 
0.4%
19.41666667353
 
0.4%
15.75353
 
0.4%
17.91666667351
 
0.4%
17.66666667350
 
0.4%
17.83333333348
 
0.4%
15.83333333347
 
0.4%
Other values (394)69237
72.1%
(Missing)16022
 
16.7%
ValueCountFrequency (%)
0.083333333332
 
< 0.1%
0.166666666710
 
< 0.1%
0.2514
 
< 0.1%
0.333333333330
< 0.1%
0.416666666729
< 0.1%
0.529
< 0.1%
0.583333333341
< 0.1%
0.666666666745
< 0.1%
0.7553
0.1%
0.833333333366
0.1%
ValueCountFrequency (%)
33.666666677
 
< 0.1%
33.5833333313
 
< 0.1%
33.536
 
< 0.1%
33.4166666771
 
0.1%
33.33333333102
0.1%
33.25139
0.1%
33.16666667155
0.2%
33.08333333161
0.2%
33179
0.2%
32.91666667219
0.2%

Occupation
Categorical

MISSING

Distinct15
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.5 MiB
Lawyer
10981 
Architect
5110 
Engineer
5040 
Scientist
5020 
Media_Manager
5003 
Other values (10)
48846 

Length

Max length13
Median length10
Mean length8.3624125
Min length6

Characters and Unicode

Total characters668993
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJournalist
2nd rowAccountant
3rd rowScientist
4th rowManager
5th rowMedia_Manager

Common Values

ValueCountFrequency (%)
Lawyer10981
11.4%
Architect5110
 
5.3%
Engineer5040
 
5.2%
Scientist5020
 
5.2%
Media_Manager5003
 
5.2%
Accountant5001
 
5.2%
Mechanic4997
 
5.2%
Journalist4962
 
5.2%
Developer4959
 
5.2%
Teacher4936
 
5.1%
Other values (5)23991
25.0%
(Missing)16022
16.7%

Length

2022-10-25T21:42:37.324192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lawyer10981
13.7%
architect5110
 
6.4%
engineer5040
 
6.3%
scientist5020
 
6.3%
media_manager5003
 
6.3%
accountant5001
 
6.3%
mechanic4997
 
6.2%
journalist4962
 
6.2%
developer4959
 
6.2%
teacher4936
 
6.2%
Other values (5)23991
30.0%

Most occurring characters

ValueCountFrequency (%)
e95220
14.2%
r74801
11.2%
a60210
 
9.0%
n59453
 
8.9%
c49748
 
7.4%
t49688
 
7.4%
i49299
 
7.4%
o24614
 
3.7%
M24530
 
3.7%
u19624
 
2.9%
Other values (18)161806
24.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter578987
86.5%
Uppercase Letter85003
 
12.7%
Connector Punctuation5003
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e95220
16.4%
r74801
12.9%
a60210
10.4%
n59453
10.3%
c49748
8.6%
t49688
8.6%
i49299
8.5%
o24614
 
4.3%
u19624
 
3.4%
h15043
 
2.6%
Other values (8)81287
14.0%
Uppercase Letter
ValueCountFrequency (%)
M24530
28.9%
L10981
12.9%
A10111
11.9%
E9971
11.7%
D9805
 
11.5%
S5020
 
5.9%
J4962
 
5.8%
T4936
 
5.8%
W4687
 
5.5%
Connector Punctuation
ValueCountFrequency (%)
_5003
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin663990
99.3%
Common5003
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e95220
14.3%
r74801
11.3%
a60210
 
9.1%
n59453
 
9.0%
c49748
 
7.5%
t49688
 
7.5%
i49299
 
7.4%
o24614
 
3.7%
M24530
 
3.7%
u19624
 
3.0%
Other values (17)156803
23.6%
Common
ValueCountFrequency (%)
_5003
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII668993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e95220
14.2%
r74801
11.2%
a60210
 
9.0%
n59453
 
8.9%
c49748
 
7.4%
t49688
 
7.4%
i49299
 
7.4%
o24614
 
3.7%
M24530
 
3.7%
u19624
 
2.9%
Other values (18)161806
24.2%

Credit_Mix
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.3 MiB
Standard
45318 
Good
19563 
Bad
15119 

Length

Max length8
Median length8
Mean length6.0769125
Min length3

Characters and Unicode

Total characters486153
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowStandard
4th rowStandard
5th rowGood

Common Values

ValueCountFrequency (%)
Standard45318
47.2%
Good19563
20.4%
Bad15119
 
15.7%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:37.540845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:37.702598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
standard45318
56.6%
good19563
24.5%
bad15119
 
18.9%

Most occurring characters

ValueCountFrequency (%)
d125318
25.8%
a105755
21.8%
S45318
 
9.3%
t45318
 
9.3%
n45318
 
9.3%
r45318
 
9.3%
o39126
 
8.0%
G19563
 
4.0%
B15119
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter406153
83.5%
Uppercase Letter80000
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d125318
30.9%
a105755
26.0%
t45318
 
11.2%
n45318
 
11.2%
r45318
 
11.2%
o39126
 
9.6%
Uppercase Letter
ValueCountFrequency (%)
S45318
56.6%
G19563
24.5%
B15119
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
Latin486153
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d125318
25.8%
a105755
21.8%
S45318
 
9.3%
t45318
 
9.3%
n45318
 
9.3%
r45318
 
9.3%
o39126
 
8.0%
G19563
 
4.0%
B15119
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII486153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d125318
25.8%
a105755
21.8%
S45318
 
9.3%
t45318
 
9.3%
n45318
 
9.3%
r45318
 
9.3%
o39126
 
8.0%
G19563
 
4.0%
B15119
 
3.1%

Payment_of_Min_Amount
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size5.0 MiB
Yes
41811 
No
28552 
NM
9637 

Length

Max length3
Median length3
Mean length2.5226375
Min length2

Characters and Unicode

Total characters201811
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNM

Common Values

ValueCountFrequency (%)
Yes41811
43.5%
No28552
29.7%
NM9637
 
10.0%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:37.867080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:38.067541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
yes41811
52.3%
no28552
35.7%
nm9637
 
12.0%

Most occurring characters

ValueCountFrequency (%)
Y41811
20.7%
e41811
20.7%
s41811
20.7%
N38189
18.9%
o28552
14.1%
M9637
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter112174
55.6%
Uppercase Letter89637
44.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y41811
46.6%
N38189
42.6%
M9637
 
10.8%
Lowercase Letter
ValueCountFrequency (%)
e41811
37.3%
s41811
37.3%
o28552
25.5%

Most occurring scripts

ValueCountFrequency (%)
Latin201811
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y41811
20.7%
e41811
20.7%
s41811
20.7%
N38189
18.9%
o28552
14.1%
M9637
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII201811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y41811
20.7%
e41811
20.7%
s41811
20.7%
N38189
18.9%
o28552
14.1%
M9637
 
4.8%

Payment_Behaviour
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size7.1 MiB
Low_spent_Small_value_payments
20313 
High_spent_Medium_value_payments
14021 
Low_spent_Medium_value_payments
11166 
High_spent_Large_value_payments
11012 
High_spent_Small_value_payments
9054 
Other values (2)
14434 

Length

Max length32
Median length31
Mean length29.4474375
Min length13

Characters and Unicode

Total characters2355795
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh_spent_Medium_value_payments
2nd rowHigh_spent_Large_value_payments
3rd rowLow_spent_Small_value_payments
4th rowLow_spent_Large_value_payments
5th rowHigh_spent_Medium_value_payments

Common Values

ValueCountFrequency (%)
Low_spent_Small_value_payments20313
21.2%
High_spent_Medium_value_payments14021
14.6%
Low_spent_Medium_value_payments11166
11.6%
High_spent_Large_value_payments11012
11.5%
High_spent_Small_value_payments9054
9.4%
Low_spent_Large_value_payments8347
8.7%
Not_Specified6087
 
6.3%
(Missing)16022
16.7%

Length

2022-10-25T21:42:38.199367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:38.376864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
low_spent_small_value_payments20313
25.4%
high_spent_medium_value_payments14021
17.5%
low_spent_medium_value_payments11166
14.0%
high_spent_large_value_payments11012
13.8%
high_spent_small_value_payments9054
11.3%
low_spent_large_value_payments8347
10.4%
not_specified6087
 
7.6%

Most occurring characters

ValueCountFrequency (%)
_301739
12.8%
e278459
11.8%
a196552
 
8.3%
p153913
 
6.5%
t153913
 
6.5%
s147826
 
6.3%
n147826
 
6.3%
l132647
 
5.6%
m128467
 
5.5%
u99100
 
4.2%
Other values (16)615353
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1894056
80.4%
Connector Punctuation301739
 
12.8%
Uppercase Letter160000
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e278459
14.7%
a196552
10.4%
p153913
8.1%
t153913
8.1%
s147826
 
7.8%
n147826
 
7.8%
l132647
 
7.0%
m128467
 
6.8%
u99100
 
5.2%
v73913
 
3.9%
Other values (10)381440
20.1%
Uppercase Letter
ValueCountFrequency (%)
L59185
37.0%
S35454
22.2%
H34087
21.3%
M25187
15.7%
N6087
 
3.8%
Connector Punctuation
ValueCountFrequency (%)
_301739
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2054056
87.2%
Common301739
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e278459
13.6%
a196552
 
9.6%
p153913
 
7.5%
t153913
 
7.5%
s147826
 
7.2%
n147826
 
7.2%
l132647
 
6.5%
m128467
 
6.3%
u99100
 
4.8%
v73913
 
3.6%
Other values (15)541440
26.4%
Common
ValueCountFrequency (%)
_301739
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2355795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_301739
12.8%
e278459
11.8%
a196552
 
8.3%
p153913
 
6.5%
t153913
 
6.5%
s147826
 
6.3%
n147826
 
6.3%
l132647
 
5.6%
m128467
 
5.5%
u99100
 
4.2%
Other values (16)615353
26.1%

Credit_Score
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing16022
Missing (%)16.7%
Memory size94.2 KiB
Standard
42575 
Poor
23124 
Good
14301 

Length

Max length8
Median length8
Mean length6.12875
Min length4

Characters and Unicode

Total characters490300
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Standard42575
44.3%
Poor23124
24.1%
Good14301
 
14.9%
(Missing)16022
 
16.7%

Length

2022-10-25T21:42:38.574748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T21:42:38.739325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
standard42575
53.2%
poor23124
28.9%
good14301
 
17.9%

Most occurring characters

ValueCountFrequency (%)
d99451
20.3%
a85150
17.4%
o74850
15.3%
r65699
13.4%
S42575
8.7%
t42575
8.7%
n42575
8.7%
P23124
 
4.7%
G14301
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter410300
83.7%
Uppercase Letter80000
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d99451
24.2%
a85150
20.8%
o74850
18.2%
r65699
16.0%
t42575
10.4%
n42575
10.4%
Uppercase Letter
ValueCountFrequency (%)
S42575
53.2%
P23124
28.9%
G14301
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin490300
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d99451
20.3%
a85150
17.4%
o74850
15.3%
r65699
13.4%
S42575
8.7%
t42575
8.7%
n42575
8.7%
P23124
 
4.7%
G14301
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII490300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d99451
20.3%
a85150
17.4%
o74850
15.3%
r65699
13.4%
S42575
8.7%
t42575
8.7%
n42575
8.7%
P23124
 
4.7%
G14301
 
2.9%

Interactions

2022-10-25T21:42:19.666751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:28.409757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:31.217144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:33.965350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:36.874577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:39.817436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:42.790530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:45.460705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:48.250320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:51.245503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:54.326800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:57.293245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:00.207378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:03.360868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:06.191676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:09.270394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:12.497675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:15.799771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:19.868515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:28.566413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:31.362422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:34.142184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:37.053098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:39.974452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:42.948154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:45.617631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:48.400811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:51.406529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:54.494073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:57.457698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:00.377637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:03.531839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:06.354875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:09.452307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:12.691427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:15.990225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:20.032573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:28.709583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:31.494971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:34.286387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:37.208320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:40.105826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:43.084626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:45.760426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:48.537830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:51.541254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:54.630710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:57.612035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:00.518796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:03.673300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:06.498647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:09.610972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:12.853671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:16.165830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:20.235283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:28.886941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:31.637145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:34.449675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:37.373498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:40.269648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:43.243217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:45.924973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:48.704065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:51.708284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:54.790304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:57.779548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:00.691301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:03.830441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:06.664302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:09.793216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:13.047577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:16.362941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:20.420619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:29.052105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:31.796055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:34.613755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:37.539137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:40.424414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:43.391988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:46.084234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:48.850441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:51.871652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:55.009882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:57.945856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:00.850143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:03.985010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:06.838922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:09.970540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:13.246272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:16.546077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:20.608506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:29.204052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:31.944146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:34.769231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:37.693631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-10-25T21:41:50.921019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:53.976869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:56.964088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:59.862505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:02.768165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:05.863529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:08.902357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:12.133639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:15.420587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:18.778681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:22.826191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:31.062008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:33.821814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:36.699458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:39.637284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:42.633645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:45.314934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:48.096156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:51.078022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:54.150171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:41:57.140651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:00.047914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:02.931072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:06.033425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:09.087036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:12.318781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:15.611685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-25T21:42:18.980138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-10-25T21:42:38.940569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-25T21:42:39.392972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-25T21:42:39.799997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-25T21:42:40.224967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-25T21:42:40.653062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-25T21:42:40.981532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-25T21:42:23.252596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-25T21:42:24.630707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-25T21:42:26.067004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-25T21:42:27.228953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexAgeAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceAuto LoanCredit-Builder LoanPersonal LoanHome Equity LoanNot SpecifiedMortgage LoanStudent LoanDebt Consolidation LoanPayday LoanNo LoanCredit_History_Age_NumericOccupationCredit_MixPayment_of_Min_AmountPayment_BehaviourCredit_Score
0020.0101399.1308535.9275001.03.02.02.014.06.04.974.01061.0642.681159132.674897114.299386856.6184670.00.01.00.00.01.00.00.00.00.024.750000JournalistGoodNoHigh_spent_Medium_value_paymentsGood
1125.0112882.6809684.8900005.01.010.04.012.010.06.240.0722.9040.090839353.616124166.446410688.4264660.01.00.01.01.00.00.01.00.00.028.916667AccountantGoodNoHigh_spent_Large_value_paymentsGood
2249.028101.4602173.4638075.07.08.03.028.08.01.854.0454.6723.607892268.268012129.181273242.9210800.01.00.00.00.00.01.01.00.00.026.750000ScientistStandardNoLow_spent_Small_value_paymentsGood
3334.022438.2701856.8558337.07.09.04.016.013.011.243.0167.5727.09418958.436308105.165644292.0836311.00.00.00.00.01.01.01.00.00.018.250000ManagerStandardNoLow_spent_Large_value_paymentsGood
4428.020975.4001943.9500002.03.09.01.02.09.09.941.0731.4026.20504216.19679267.158201361.0400071.00.00.00.00.00.00.00.00.00.024.250000Media_ManagerGoodNMHigh_spent_Medium_value_paymentsGood
5533.031751.8302574.9858335.04.018.03.024.012.018.768.0110.7324.30192258.006937120.397883369.0937630.01.00.00.00.00.01.00.01.00.024.083333Media_ManagerStandardYesNot_SpecifiedGood
6646.08052.790961.0658338.07.024.08.045.023.016.2812.03937.8639.17860529.240525129.181273320.5385711.00.01.00.01.00.01.01.01.00.06.500000MusicianBadYesNot_SpecifiedGood
7730.016568.4251550.7020835.03.018.01.014.08.03.565.0478.1140.9139148.47408623.761873372.8342500.00.00.00.00.00.00.00.01.00.029.333333EntrepreneurStandardNoHigh_spent_Medium_value_paymentsStandard
8848.032228.6702819.6033757.06.08.04.025.010.012.004.0940.3628.589412176.204872145.824991327.8615121.01.00.00.00.00.00.00.00.00.032.666667WriterStandardNMHigh_spent_Small_value_paymentsNaN
9935.019328.7101480.7258333.05.02.02.09.02.07.273.0160.0325.22156325.87483076.099156336.0985970.01.00.00.00.00.01.00.00.00.018.250000ArchitectGoodNoLow_spent_Small_value_paymentsGood

Last rows

df_indexAgeAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceAuto LoanCredit-Builder LoanPersonal LoanHome Equity LoanNot SpecifiedMortgage LoanStudent LoanDebt Consolidation LoanPayday LoanNo LoanCredit_History_Age_NumericOccupationCredit_MixPayment_of_Min_AmountPayment_BehaviourCredit_Score
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